Systems and methods for intelligent ticket management and resolution

ABSTRACT

Some aspects of the present disclosure are directed to computer-implemented systems and methods for efficient ticket resolution. The methods may include: receiving a request to resolve an issue; analyzing, via natural language processing, the language in the request to determine the issue to be resolved; determining whether the issue meets a condition for automated resolution; if the condition is met: extracting, via an application programming interface and from the at least one user device, information needed to resolve the issue; and resolving the issue using the extracted information; and if the condition is not met: generating a ticket; assigning a work group to the ticket; determining whether a job aid associated with the issue exists; and forwarding at least one of: the job aid; received communications from the work group; and an estimated amount of time to resolution.

TECHNICAL FIELD

The present disclosure generally relates to computerized methods andsystems for intelligent ticket management and resolution and, moreparticularly, to computerized methods and systems for automaticallyassessing the resolvability and characteristics of an issue based onuser-provided issue descriptions and efficiently providing solutions tothe user based on the assessment.

BACKGROUND

Many service providers (e.g., financial service providers) providecomputerized products and services (e.g., banking applications andservices) to customers. In some cases, a customer may run into issueswith a product or service and may be required to consult with theservice provider in order to resolve the issue. In many cases, customersopen support cases (i.e., “tickets”) with the service provider bycontacting them, for example, via phone or via a web-based application.In conventional systems, the service provider will require certaininformation about the customer's issue that enable the ticket to beaddressed by an appropriate work group or team associated with theservice provider. This information can be acquired, for example, throughelectronic or telephonic communications, where the service providerprompts the customer to provide the information.

This initial exchange of information to determine the specific issueidentified by the customer and identifying appropriate work groupstypically requires a large amount of manual assessment by the serviceprovider, which will need to expend electronic resources and labor hoursin addressing the customer's issues. In many cases, the service providerwill provide service and product manuals and other job aids (e.g.,Frequently Asked Questions) that are accessible through a website inorder to mitigate these issues. Customers, however, often find itdifficult to navigate through these resources to find the correct jobaid and are more likely to open a ticket with the service provider thanconsult these resources, even though the solution may have beenaccessible through an available job aid.

Therefore, there is a need for systems and methods that enableautonomous computer systems to efficiently resolve service and productrelated customer issues by intelligently evaluating user-provided issuedescriptions and directing customers to appropriate work groups and/orjob aides.

SUMMARY

One aspect of the present disclosure is directed to acomputer-implemented system for efficient ticket resolution. The systemmay include: a memory storing instructions; and at least one processorconfigured to execute the instructions to perform operations including:receiving, from at least one user device, a request to resolve an issue,the request comprising language; analyzing, via natural languageprocessing, the language in the request to determine the issue to beresolved; based on the analysis, determining whether the issue meets acondition for automated resolution; if the condition is met: extracting,via an application programming interface and from the at least one userdevice, information needed to resolve the issue; and resolving the issueusing the extracted information; and if the condition is not met:inserting, into at least one database, a ticket comprising: anidentifier associated with the at least one user device; and informationassociated with the issue to be resolved; assigning, based on theinformation, a work group to the ticket; consulting the at least onedatabase to determine whether a job aid associated with the issueexists; forwarding, to the at least one user device, at least one of:the job aid; received communications from the work group; and anestimated amount of time to resolution based on the information.

Another aspect of the present disclosure is directed to acomputer-implemented method for efficient ticket resolution. The methodmay include: receiving, from at least one user device, a request toresolve an issue, the request comprising language; analyzing, vianatural language processing, the language in the request to determinethe issue to be resolved; based on the analysis, determining whether theissue meets a condition for automated resolution; if the condition ismet: extracting, via an application programming interface and from theat least one user device, information needed to resolve the issue; andresolving the issue using the extracted information; and if thecondition is not met: inserting, into at least one database, a ticketcomprising: an identifier associated with the at least one user device;and information associated with the issue to be resolved; assigning,based on the information, a work group to the ticket; consulting the atleast one database to determine whether a job aid associated with theissue exists; forwarding, to the at least one user device, at least oneof: the job aid; received communications from the work group; and anestimated amount of time to resolution based on the information.

Other systems, methods, and computer-readable media are also discussedherein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an example server computer system for intelligentticket management and resolution, consistent with embodiments of thisdisclosure.

FIG. 2 is a diagram of an example structure of a system for intelligentticket management and resolution, consistent with embodiments of thisdisclosure.

FIG. 3 is a diagram of an example structure of an UI controller systemfor intelligent ticket management and resolution, consistent withembodiments of this disclosure.

FIG. 4 is a diagram of an example structure of an intelligent ticketingadvisor system for intelligent ticket management and resolution,consistent with embodiments of this disclosure.

FIG. 5 is a flow diagram of an exemplary process for intelligent ticketmanagement and resolution, consistent with embodiments of thisdisclosure.

DETAILED DESCRIPTION

The disclosed embodiments include systems and methods for intelligentticket management and resolution. Before explaining certain embodimentsof the disclosure in detail, it is to be understood that the disclosureis not limited in its application to the details of construction and tothe arrangements of the components set forth in the followingdescription or illustrated in the drawings. The disclosure is capable ofembodiments in addition to those described and of being practiced andcarried out in various ways.

As such, those skilled in the art will appreciate that the conceptionupon which this disclosure is based may readily be utilized as a basisfor designing other structures, methods, and systems for carrying outthe several purposes of the present disclosure.

Reference will now be made in detail to the present example embodimentsof the disclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

FIG. 1 is a block diagram of an example server computer system 100(referred to as “server 100” hereinafter), consistent with someembodiments of this disclosure. Server 100 may be one or more computingdevices configured to execute software instructions stored in memory toperform one or more processes consistent with some embodiments of thisdisclosure. For example, server 100 may include one or more memorydevices for storing data and software instructions and one or morehardware processors to analyze the data and execute the softwareinstructions to perform server-based functions and operations (e.g.,back-end processes). The server-based functions and operations mayinclude intelligent ticket management and resolution.

In FIG. 1 , server 100 includes a hardware processor 110, aninput/output (I/O) device 120, and a memory 130. It should be noted thatserver 100 may include any number of those components and may furtherinclude any number of any other components. Server 100 may bestandalone, or it may be part of a subsystem, which may be part of alarger system. For example, server 100 may represent distributed serversthat are remotely located and communicate over a network.

Processor 110 may include or one or more known processing devices, suchas, for example, a microprocessor. In some embodiments, processor 110may include any type of single or multi-core processor, mobile devicemicrocontroller, central processing unit, or any circuitry that performslogic operations. In operation, processor 110 may execute computerinstructions (e.g., program codes) and may perform functions inaccordance with techniques described herein. Computer instructions mayinclude routines, programs, objects, components, data structures,procedures, modules, and functions, which may perform particularprocesses described herein. In some embodiments, such instructions maybe stored in memory 130, processor 110, or elsewhere.

I/O device 120 may be one or more devices configured to allow data to bereceived and/or transmitted by server 100. I/O device 120 may includeone or more customer I/O devices and/or components, such as thoseassociated with a keyboard, mouse, touchscreen, display, or any devicefor inputting or outputting data. I/O device 120 may also include one ormore digital and/or analog communication devices that allow server 100to communicate with other machines and devices, such as other componentsof server 100. I/O device 120 may also include interface hardwareconfigured to receive input information and/or display or otherwiseprovide output information. For example, I/O device 120 may include amonitor configured to display a customer interface.

Memory 130 may include one or more storage devices configured to storeinstructions used by processor 110 to perform functions related todisclosed embodiments. For example, memory 130 may be configured withone or more software instructions associated with programs and/or data.

Memory 130 may include a single program that performs the functions ofthe server 100, or multiple programs. Additionally, processor 110 mayexecute one or more programs located remotely from server 100. Memory130 may also store data that may reflect any type of information in anyformat that the system may use to perform operations consistent withdisclosed embodiments. Memory 130 may be a volatile or non-volatile(e.g., ROM, RAM, PROM, EPROM, EEPROM, flash memory, etc.), magnetic,semiconductor, tape, optical, removable, non-removable, or another typeof storage device or tangible (i.e., non-transitory) computer-readablemedium.

Consistent with some embodiments of this disclosure, server 100 includesticketing controller system 112 that may include a UI controller system114 and an intelligent ticketing advisor system 116. Ticketingcontroller may be configured to autonomously and automatically implementintelligent ticket management and resolution using UI controller system114 and intelligent ticketing assessor 116. Ticketing controller 112 maybe implemented as software (e.g., program codes stored in memory 130),hardware (e.g., a specialized chip incorporated in or in communicationwith processor 110), or a combination of both. UI controller system 114may be configured to react to various user-initiated events (e.g., aninput received from user interface 150). Intelligent ticketing advisor116 may be configured to evaluate user-provided descriptions, forexample, using natural language processing (NLP) and artificialintelligence (AI).

Server 100 may also be communicatively connected to one or moredatabases 140. For example, server 100 may be communicatively connectedto database 140. Database 140 may be a database implemented in acomputer system (e.g., a database server computer). Database 140 mayinclude one or more memory devices that store information (e.g., thedata outputted by ticketing controller system 112) and are accessedand/or managed through server 100. By way of example, database 140 mayinclude Oracle™ databases, Sybase™ databases, or other relationaldatabases or non-relational databases, such as Hadoop sequence files,HBase, or Cassandra. Systems and methods of disclosed embodiments,however, are not limited to separate databases. In one aspect, server100 may include database 140. Alternatively, database 140 may be locatedremotely from the server 100. Database 140 may include computingcomponents (e.g., database management system, database server, etc.)configured to receive and process requests for data stored in memorydevices of database 140 and to provide data from database 140.

Server 100 may also be communicatively connected to at least one userinterface 150. User interface 150 may include a graphical interface(e.g., a display panel), an audio interface (e.g., a speaker), or ahaptic interface (e.g., a vibration motor). For example, the displaypanel may include a liquid crystal display (LCD), a light-emitting diode(LED), a plasma display, a projection, or any other type of display. Theaudio interface may include microphones, speakers, and/or audioinput/outputs (e.g., headphone jacks). In some embodiments, userinterface 150 may be included in server 100. In some embodiments, userinterface 150 may be included in a separate computer system. Userinterface 150 may be configured to display data transmitted from server100.

In connection with server 100 as shown and described in FIG. 1 , thesystems and methods as described herein may provide a technical solutionto technical problems in automated ticket management and resolution.Aspects of this disclosure may relate to autonomous testing of acomputer application, including systems, apparatuses, methods, andnon-transitory computer-readable media. For ease of description, asystem is described below, with the understanding that aspects to thesystem apply equally to methods, apparatuses, and non-transitorycomputer-readable media. For example, some aspects of such a system canbe implemented by a system (e.g., server 100 and database 140), by anapparatus (e.g., server 100), as a method, or as program codes orcomputer instructions stored in a non-transitory computer-readablemedium (e.g., memory 130 or another storage device of server 100). In abroadest sense, the system is not limited to any particular physical orelectronic instrumentalities, but rather can be accomplished using manydifferent instrumentalities.

Consistent with some embodiments of this disclosure, a system forintelligent ticket management and resolution may include anon-transitory computer-readable medium configured to store instructionsand at least one processor configured to execute the instructions toperform operations. A computer application, as used herein, may refer toa set of computer programs or modules combined in a logical manner toimplement a function (e.g., a financial service). In some embodiments,the computer application may be created, maintained, updated, orexecuted at a server computer of the system. In some cases, because thefunction may be implemented by multiple, different sequences ofoperations the computer application may be implemented by multiple,different programs.

By way of example, with reference to FIG. 1 , the system may includeserver 100 and database 140. The at least one processor may be processor110 in server 100. The non-transitory computer-readable medium may bememory 130 in server 100. The instructions stored in the non-transitorycomputer-readable medium may be used for implementing testing module 111in server 100.

FIG. 2 is a diagram of an example structure of a system 200 forintelligent ticket management and resolution, consistent with someembodiments of this disclosure. By way of example a user (e.g., acustomer) associated with user device 201 (e.g., a computer, tablet,cellular phone, etc.) may experience issues with a product or serviceassociated with a service provider (e.g., financial services) thatimplements the systems and methods disclosed herein to resolve customerissues. In some embodiments, user device 201 may be associated with acustomer that initiates a support case by providing, via a network, atext or voice description of their issue to chatbot UI 210, digitalcollaboration app UI 220, ticketing system UI 230, or any other suitablemethod of communicating with the service provider and may be required toconsult with the service provider in order to resolve the issue. In someembodiments, however, user device 201 may be associated with an employeeor agent associated with the service provider that is acting on behalfof a customer to help resolve their issue. For example, a customer maybe in communication with a user associated with user device 201, anduser device 201 may provide updates to ticketing system UI 230 based onthe communications.

In some embodiments, chatbot UI 210 may include a software or web-basedapplication that may be used to conduct an online chat conversation withuser device 201 via text or text to speech, and may be configured tosimulate the way a human would act as a conversational partner. Digitalcollaboration app UI 220 may include a software or web-based application(e.g., Slack, Microsoft Teams, Google Drive, etc.) that offers servicessuch as instant messaging, file sharing, teleconferencing, and/or videoconferencing, or any other application that enables user device 201 tointeract with one or more individuals associated with the serviceprovider. Digital collaboration app UI may also be configured to viewand update ticket information through UI controller system 114. ChatbotUI 210 and digital collaboration app UI may be configured to exchangeinformation, such as user-provided descriptions and/or language andsystem responses, between user device 201 and UI controller system 114.Ticketing system UI 230 may be any software or web-based applicationthat user device 201 may use to update or modify information relating totheir ticket status (e.g., not resolved, resolved, estimated resolutiontime, etc.). Ticketing system UI 230 may be communicatively coupled toticketing system 240 (e.g., via a network).

In some embodiments, UI controller system 114 may be configured to reactto various user-initiated events through chatbot UI 210 and digitalcollaboration app UI 220. For example, UI controller system 114 mayconduct a dialogue with user device 201 and communicate with ticketingsystem 240, intelligent ticketing advisor system (“ITA”) 116, and/orvarious systems of record 270 in order to identify issues, create, view,and update tickets, and to resolve issues in real-time. These varioussystems of record may include systems responsible for storing andmanaging client data. For example, these systems may often need to beupdated to correct data entry errors, or update configuration to enableadditional system capabilities to be implemented. Examples of suchsystems include those responsible for maintaining client's customer'saccount data, and/or client-user ids, passwords, and permissions grantedto users. Chatbot UI, digital collaboration app UI 220, ticketing systemUI 230, ticketing system 240, UI controller system 114, intelligentticket advisor system 116, various systems of record 270, and/or anyother system, subsystem, or component associated with system 200 may beimplemented on a single system and/or server or as multiple systemsinterconnected via a network.

In some embodiments, ITA 116 may be configured using natural languageprocessing (NLP) technology, and may be configured to perform varioustasks, such as text and speech processing, morphological analysis,syntactic analysis, lexical and rational semantics, discourse, or anyother form of analysis of natural language data. ITA 116 may alsoutilize dialogue management, natural language generation, or any othersuitable means of generating natural language responses or automaticallyanswering questions based on user-provided information.

In some embodiments, ITA 116 may be configured to evaluate auser-provided description of an issue received from chatbot UI 210,digital collaboration app UI, and/or ticketing system UI 230 (FIG. 2 ).Based on the evaluation, ITA 116 may identify the issue and generate anassessment of how the issue can be most efficiently resolved. ITA 116may continuously evaluate the ticket and update the assessment or maygenerate a new assessment upon receiving additional information from UIcontroller system 114 (e.g., additional user-provided descriptions) orfrom ticketing system 240 (e.g., ticket status updates). In someembodiments, the assessment may include an identification of the issue,a determination of whether the issue is immediately resolvable or can beresolved in real time, whether additional information is needed, and/oran estimated resolution time. ITA 116 may store historical ticket dataand recorded issue-resolution time 280 (FIG. 2 ) in at least onedatabase (e.g., database 140 in FIG. 1 ), which may be used, forexample, to train one or more machine-learning algorithms utilized byITA 116 (e.g., support vector machines, Bayesian networks, maximumentropy, conditional random field, random forest, gradient boosting,neural networks, etc.). In some embodiments, ITA 116 may be configuredto access the at least one database to determine whether there is a jobaid (e.g., product and/or service manuals, FAQs, troubleshooting guides,etc.) corresponding to the identified issues, and may provide userdevice 201, for example via chatbot UI 210 and/or digital collaborationapp UI 220, the job aid and/or excerpt of the job aid corresponding tothe issue (e.g., by sending a file or a hyperlink).

FIG. 3 is a diagram of an example structure of UI controller system 114for intelligent ticket management and resolution, consistent with someembodiments of this disclosure. UI controller system 114 may includereal-time dialog controller sub-system 310, which may include dialogmodule 312 and dialog resolution module 314. It is to be understood thatthe configuration of elements depicted in FIG. 3 is exemplary only, andthat disclosed embodiments are not limited to any particular programmingor arrangement of modules, devices, and/or systems.

Dialog module 312 may be configured to receive user-defined issuedescriptions as free-form text from chatbot UI 210 and send responses tochatbot UI 210. Dialog module 312 may also be communicatively coupledwith ticketing system 240 and various systems of record 270, forexample, via an application-programming interface (API).

Dialog resolution module 314 may be configured to send the user-definedissue description received by dialog module 312 to ITA 116, which mayreturn an assessment to dialog resolution module 314.

In some embodiments, UI controller system may also include digitalcollaboration app controller sub-system 320, which may includeticket-data module 322, user-state module 324, and alert-delivery module326.

Ticket interface module may be configured to retrieve a job aid fromdigital collaboration app UI 220 and forward ticket-related information(e.g., an identified issue in the ticket, an assigned work group,associated job aids, estimated time to resolution, communications fromthe user and the work group, etc.) to user-state module 324 andticketing system 240.

User-state module 324 may be configured to receive information fromticket-data module 322 and alert-delivery module and may maintain,monitor, and/or update ticket information based on the receivedinformation. User-state module 324 may be responsible for relating useridentity within the digital collaboration application itself, to thesame user's identity within the ticketing system. When an alert isgenerated based on a ticket-update, for example, user-state module 324may enable the alert to be delivered to the digital collaborationinstance associated with that user.

Alert-delivery module 326 may be configured to send alerts to digitalcollaboration app UI 220 based on information received from user-statemodule 324 or intelligent ticketing advisor system 116. Alerts mayinclude any notification relating to a status of a ticket. For example,alert-delivery module 326 may be configured to send digitalcollaboration app UI 220 an alert indicating that the ticket has beenresolved, that a work group has been assigned to the ticket, that anestimated time to resolution has been updated, that an additional issuehas been identified, and/or that a suitable job aid has been found.

FIG. 4 is a diagram of an example structure of ITA 116 for intelligentticket management and resolution, consistent with some embodiments ofthis disclosure. ITA 116 may include controller sub-system 410, job aidmanagement system 420, NLP assessment sub-system 430, and NLP assessmenttraining sub-system, and may be configured to send and receive job aidinformation from job aid management sub-system 420. Controllersub-system 410 may include interface module 412, event module 414, andactivity logging module 416. It is to be understood that theconfiguration of elements depicted in FIG. 4 is exemplary only, and thatdisclosed embodiments are not limited to any particular programming orarrangement of modules, devices, and/or systems.

Interface module 412 may be configured to exchange user-defined issuedescriptions, new ticket data, and description assessments betweenticketing system 240, UI controller system 114, event module 414,activity logging module 416, and NLP assessment sub-system 430. Forexample, interface module 412 may be configured to forward updatedticket data received from NLP assessment sub-system 430 to ticketingsystem 240.

Event module 414 may be configured to monitor the exchange of datathrough interface module 412 and forward event messages to UI controllersystem 114, such as an alert regarding the status of a ticket. In someembodiments, the alerts may be interactive in form. For example, eventmodule 414 may be configured to prompt a user to submit additionalinformation that may be added to a given ticket stored and/or monitoredby ticketing system 240.

Activity logging module 416 may collect data from interface module toNLP assessment training sub-system 440 to be used as training data toimprove NLP operations of ITA 116. In some embodiments, activity loggingmodule 416 may be configured to capture user-activity occurring via UIcontroller system 114. This activity includes, for example, user-choicesrelative to suitability of a job aid to satisfying user needs. Theseactivity data may be fed to the NLP assessment training sub-system 440in order to enable more effective future recommendations to be made byNLP assessment sub-system 430 through incremental NLP model training. Insome embodiments, job aid management sub-system 420 may includeinterface module 422 and storage module 424, and may be configured tomaintain a record of job aids and facilitate the access, insertion,deletion, and/or modification of job aid or job aid-related information.Storage module 424 may be a memory, a database, or any other form ofdata storage that may store any number of job aids. Interface module 422may be configured to access storage module 424 to determine whether ajob aid for a certain issue, retrieve the job aid, and transmit the jobaid to UI controller system 114. For example, NLP assessment system mayevaluate a user-defined issue description and return an assessmentincluding an identified issue to UI controller system 114 throughinterface module 412. UI controller system 114 may forward theidentified issue to interface module 422, which may determine whether ajob aid corresponding to the identified issue exists.

In some embodiments, NLP assessment sub-system 430 may include featureextraction module 432, entity extraction module 434, and classifiermodule 436. Feature extraction module 432 may be configured to filter orpre-process the information contained in the user-defined issuedescription into identifiable individual properties or characteristics,such as a combination of one or more words or characters. Featureextraction module 432 may be configured to perform a variety of complexdata analysis techniques, such as text transformation, vectorization,independent component analysis, latent semantic analysis, partial leastsquares, principal component analysis, multifactor dimensionalityreduction, nonlinear dimensionality reduction, multilinear principalcomponent analysis, or any other appropriate dimensionality reductiontechnique.

Entity extraction module 434 may be configured to identify and classifykey elements in the user-defined issue description from text into one ormore pre-defined categories. For example, entity extraction module 434may be configured to locate and classify named entities mentioned in theuser-provided issue description into pre-defined categories such asperson names, device names, network names, organizations, locations,time expressions, quantities, monetary values, percentages, or any othercategory of entities. In some embodiments, entity extraction module 434may utilize extraction rules that may be based one or more extractiontechniques, such as pattern matching, linguistics, syntax, and/orsemantics.

Classifier module 436 may be configured to determine, based on theprocessed user-defined issue description, a specific issue, aclassification of the specific issue, and/or any other characteristicscorresponding the user-defined issue description. In general, NLPassessment sub-system and/or components thereof may be configured totransform the user-defined issue description into structured,computer-readable data and assess the transformed data using data modelsand/or machine learning algorithms (e.g., support vector machines,Bayesian networks, maximum entropy, conditional random field, randomforest, gradient boosting, neural networks, etc.) that are trained byNLP assessment training sub-system 440.

In some embodiments, NLP assessment training subsystem 440 may includedata cleaning module 442 and model training module 444. NLP assessmenttraining subsystem 440 may be configured to receive data from activitylogging module 416, such as information exchanged through interfacemodule 412. Data cleaning module 442 may be configured to filter thedata received from activity logging module 416 and remove noise (e.g.,hashtags, punctuation, numbers, etc.) that may enable model trainingmodule 444 to more easily detect patterns in the data. After the data isfiltered by data cleaning module 442, model training module 444 may beconfigured to process the data received from activity logging module 416to generate data models and/or train one or more machine learningalgorithms (e.g., support vector machines, Bayesian networks, maximumentropy, conditional random field, random forest, gradient boosting,neural networks, linear classifiers, etc.) related to the NLPcapabilities of ITA 116, The NLP data models generated by NLP assessmenttraining sub-system 440 may be utilized by NLP assessment sub-system 430to evaluate user-defined issue descriptions in order to provideinterface module 412 with an assessment of the issue description.

FIG. 5 is a flow diagram of exemplary process 500 for intelligent ticketmanagement and resolution, consistent with some embodiments of thisdisclosure. In some embodiments, process 500 may be executed by one ormore processors (e.g., processor 110) associated with system 200, and/orexecuted either partially or wholly by the one or more components ofsystem 200 (e.g., UI controller system 114, ITA 116, real-time dialogcontroller sub-system 310, NLP assessment sub-system 430, classifiermodule 436, etc.). For ease of discussion, process 500 will be describedas being executed by UI controller system 114 and/or ITA 116, althoughit is to be understood that process 500 may be executed by any suitablecomponent or sub-component of system 200, consistent with the presentdisclosure.

In some embodiments, process 500 may begin at step 510. At step 510, ITA116 may receive, from at least one user device, a request to resolve anissue. For example, through chatbot UI 210 or digital collaboration appUI 220, user device 201 may send a request to UI controller system 114,which may forward the request to ITA 116 for assessment. In someembodiments, the request may include language to be analyzed by ITA 116.The language contained in the request is not limited to a single form ofcommunication. For example, the language contained the request may be inthe form of free form text, spoken language, or any other appropriatecommunication medium. In some embodiments, UI controller system 114 maybe configured to instantiate a digital dialogue session (e.g., a chatbotsession) with user device 201. The digital dialogue session may be usedto facilitate communication between user device 201 and UI controllersystem 114. For example, the request to resolve an issue may be sent toUI controller system 114 through the digital dialogue session, and UIcontroller system 114 return responses and prompts to user device 201through the digital dialogue session.

At step 520, ITA 116 may analyze, via natural language processing(“NLP”), the language in the request to determine the issue to beresolved. For example, ITA 116 may be programmed or otherwise configuredto perform tasks such as speech and/or text recognition, naturallanguage understanding, and natural language generation. For example, insome embodiments, ITA 116 may feed the request to at least onemachine-learning algorithm. These tasks may be carried out through theuse of one or more machine-learning algorithms that use historicalticket data to analyze the language and return an assessment of thelanguage to ITA 116. In some embodiments, the at least onemachine-learning algorithm may be implemented using statistical methods,machine learning, or neural networks. For example, in some embodiments,the machine-learning algorithm may include at least one of a generalizedleast squares regression technique, an ordinary least squares regressiontechnique, a tree technique (random forest, decision tree, etc.), agradient boosting technique, a neural network technique, a supportvector machine technique, or a linear classifier technique.

In some embodiments, ITA system 116 may train the at least one machinelearning algorithm using the historical ticket data. The historical datamay include previously received requests, user-provided issuedescriptions in previous requests, previously assigned work groups foreach previously received requests, recorded resolution times for eachpreviously received requests, documentation associated (i.e., job aids)with at least one of a product, service, or application associated withthe previously received requests, or any other information exchangedbetween users and UI controller system 114 associated with previoustickets. For example, NLP assessment training sub-system 440 (i.e., datacleaning module 442 and model training module 444) may train the atleast one machine learning algorithm using information received fromactivity logging module 416 in order to improve the ability of NLPassessment sub-system 430 (i.e., feature extraction module 432, entityextraction module 434, and classifier module 436) to make accurateassessments based on the user-defined issue descriptions contained inthe requests.

ITA 116 may identify the particular issue described by the user based onthe analysis completed at step 520. For example, ITA 116 may determinethat user device 201 is experiencing a particular problem (e.g., anissue accessing content in an application associated with a serviceprovider) based on the description provided. At step 530, ITA 116 maydetermine whether the particular issue identified meets a condition forautomated resolution. In some embodiments, the condition may besatisfied if ITA 116 is able to determine that the particular issue maybe immediately resolved. ITA 116 may make this determination, forexample, by consulting a record stored in at least one database (e.g.,database 140) of pre-defined issues and associated solutions, or it maybe included in the assessment generated by the at least onemachine-learning algorithm implemented by NLP assessment sub-system 430.If the condition is met, process 500 may proceed to step 540. If thecondition is not met, process 500 may proceed to step 550.

At step 530, ITA 116 may determine whether additional information isneeded to resolve the issue. For example, in step 530, ITA 116 maydetermine that system 200 may immediately resolve the user's inabilityto access content in a web-based application associated with the serviceprovider if additional information (e.g., account information, technicalspecifications of the at least one user device, network information,etc.) is acquired. If additional information is needed, process 500 mayproceed to step 540. At step 540, ITA 116 may extract information fromuser device 201 through one or more communication channels, such aschatbot UI 210 or digital collaboration app UI 220. For example, if ITA116 determines that an email address is required to resolve the issue,ITA 116 may generate an automated response or prompt requesting theemail address and transmit the response or prompt to user device 201through a digital dialogue session facilitated by chatbot UI 210. Afterreceiving the automated response or prompt from ITA 116, user device 201may return the required information to ITA 116 through chatbot UI 210.If ITA 116 determines that no additional information is needed at step530, process 500 may proceed directly to step 545 from step 530.

Once ITA 116 receives all of the information necessary to resolve, theissue, ITA 116 may resolve the issue at step 545. For example, ITA 116may send user instructions to user device 201 that a user may follow toimmediate resolve the issue. In some embodiments, ITA 116 may transmitinstructions to user device 201 that cause user device 201 to executeoperations that resolve the issue. The issue may thus be resolvedimmediately without opening a ticket, therefore eliminating the need forassigning a work group and reducing the amount of resources used onfacilitating communication between user device 201 and the work group.For example, an administrative, user with authority to reset a user'sapplication passwords within some particular system of record may bymeans of interaction with chatbot U 210 cause a user's password to bereset by means of interaction between dialog module 312 and that systemof record, and thereby obviate the need to create a ticket. As a furtherexample, and by means of similar systemic interaction, an authorizeduser may cause a system of record to be updated enabling a particularfeature or function of that application to be made available to users ofthat system.

If ITA 116 determines at step 530 that the condition for resolution isnot met, process 500 may proceed to step 550. At step 550, ITA 116 maybe configured to determine whether a job aid associated with the issueexists. For example, ITA 116 may consult at least one database (e.g.,database 140) associated with system 200 to determine if a manual, FAQ,or other documentation associated with the issue identified at step 520exists. In some embodiments, ITA 116 may consult the at least onedatabase by conducting a search for one or more keyword-derived featuresassociated with the identified issue or included in the user-providedissue description. For example, ITA 116 may retrieve all job aidsrelating to or containing the keyword “network” if user device 201 isexperiencing network related issues. In some embodiments, ITA 116 cannarrow search results by additional keyword-derived features containedin the user-provided issue description, such as “slow”, “wireless”, orother issue-related descriptors. The at least one database may alsoinclude a record of job aid associated with particular issues, and ITA116 may determine that a job aid exists for the issue identified in step520 based on the record. If the job aid exists UI controller system 114may retrieve the job aid, for example, from interface module 422 of jobaid management sub-system 420.

By way of example, job aid identification may include one or moreoutputs from the assessment performed by ITA 116 of the user-providedissue description provided by interaction with dialog resolution module314. In some embodiments, this process may include a priori naturallanguage processing of job aid content to create vectorizedrepresentation of the content. Similar NLP vectorization ofuser-provided issue description may be performed when a user expressesan issue by interacting with chatbot UI 210. By evaluating the latteragainst similar values in the set of all job aids, the system mayrecommend an appropriate job aid based on a quantitative assessment ofthe vectorized representations and an arbitrary threshold.

If ITA 116 determines that a relevant job aid exists at step 550,process 500 may proceed to step 560. At step 560, ITA 116 may send therelevant job aid to the at least one user device user at step 560 (e.g.,through chatbot UI 210, digital collaboration app UI 220, and/orticketing system UI 230). At step 562, ITA 116 may determine whether thesent job aid resolved or helped resolved the issue. For example, ITA 116may send an alert or notification to the at least one user deviceprompting the user to indicate whether the issue has been resolved. ITA116 may then receive a response from the at least one user device anddetermine, based on the response, whether the issue has been resolved.If the issue has been resolved, process 500 may conclude at step 564. Atstep 564, ITA 116 may determine that no further action is need. In someembodiments, ITA 116 may be configured to terminate any existingcommunication channels with the at least one user device, and/or maycause on or more systems and/or subsystems in system 200 to terminateany processes related to the issue.

If ITA determines that a relevant job aid does not exist at step 550, orif the issue was not resolved by the job aid at step 562, process 500may proceed to step 552. At step 552, ITA 116 may generate a ticketassociated with the issue. In some embodiments, ITA 116 may insert theticket into a record of tickets stored in at least one databaseassociated with system 200 (e.g., database 240). ITA 116 may also sendthe ticket to ticketing system 240, which may be configured to monitorthe status of the ticket and update ticket information based oninformation received from ticketing system UI 230, UI controller system114, or ITA 116. In some embodiments, the generated ticket may includean identifier associated with user device 201, information associatedwith the issue to be resolve (e.g., the particular identified issue,user-provided issue descriptions, etc.), an estimated resolution time,or any other form of information relating to the ticket that may beprovided by user device 201 or generated by ITA 116.

At step 554, ITA 116 may assign a work group to the ticket. A work groupmay be one or more workers associated with a service provider associatedwith system 200 that are tasked with aiding customers (e.g., a userassociated with user device 201) with a particular issue or grouping ofissues. For example, there may be one or more work groups associatedwith system 200 that are assigned to help resolve technical issues, andthere may be one or more separate work groups that are assigned to helpresolve logistical issues. In some embodiments, the work group may be atleast partially based on the assessment of the issue and/or the analysiscompleted by ITA 116 at step 520. For example, ITA 116 may determinethat user device 201 is experiencing a particular issue and may assignthe ticket to a work group tasked with resolving that particular issue.For example, ITA 116 may identify that a customer is experiencingconnectivity issues based on a description including “can't connect tothe internet” and may assign the ticket to a work group associated withnetwork issues. In some embodiments, the assignment may also be based onthe availability of one or more work groups. For example, ITA 116 may beconfigured to consult a record stored in a database (e.g., database 140)indicating the availability and/or workload of work group and to biasthe assignment of tickets to work groups with more availability. In someembodiments, UI controller system 114 may establish a communication linkbetween the at least one user and the assigned work group. For example,UI controller system 114 and/or digital collaboration app UI 220 mayopen a communication channel through a digital collaboration application(e.g., Slack, Microsoft Teams, Google Drive, etc.) that allows userdevice 201 to communicate and exchange information with the assignedwork group.

At step 556, UI controller system 114 may forward ticket-relatedinformation to at least one user device associated with user device 201(e.g., through chatbot UI 210, digital collaboration app UI 220, and/orticketing system UI 230). Ticket-related information may include, forexample, the job aid retrieved at step 550, received communications fromthe work group, an estimated amount of time to resolution that isestimated by ITA 116, or any other information associated with theticket generated at step 550. UI controller system 114 may be configuredto forward the ticket-related information on a continuing basis, forexample, at predetermined time intervals or upon receiving a requestfrom user device 201 to provide a status update.

Disclosed embodiments may include a non-transitory computer-readablemedium that stores instructions for a processor (e.g., processor 110)for autonomous testing of a computer application in accordance with theexample flowchart of FIG. 5 above, consistent with embodiments in thepresent disclosure. For example, the instructions stored in thenon-transitory computer-readable medium may be executed by the processorfor performing process 500 in part or in entirety. Common forms ofnon-transitory media include, for example, a floppy disk, a flexibledisk, hard disk, solid-state drive, magnetic tape, or any other magneticdata storage medium, a Compact Disc Read-Only Memory (CD-ROM), any otheroptical data storage medium, any physical medium with patterns of holes,a Random Access Memory (RAM), a Programmable Read-Only Memory (PROM),and Erasable Programmable Read-Only Memory (EPROM), a FLASH-EPROM or anyother flash memory, Non-Volatile Random Access Memory (NVRAM), a cache,a register, any other memory chip or cartridge, and networked versionsof the same.

While the present disclosure has been shown and described with referenceto particular embodiments thereof, it will be understood that thepresent disclosure can be practiced, without modification, in otherenvironments. The foregoing description has been presented for purposesof illustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. Various programs orprogram modules can be created using any of the techniques known to oneskilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

What is claimed is:
 1. A computer-implemented system for efficientticket resolution comprising: a memory storing instructions; and atleast one processor configured to execute the instructions to performoperations comprising: receiving, from at least one user device, arequest to resolve an issue, the request comprising language; capturing,using an activity logging module of a ticket management server, useractivity occurring at the at least one user device; analyzing, using theticket management server, the language in the request to determine theissue to be resolved by performing natural language processing on thelanguage wherein performing the natural language processing comprisesutilizing at least one machine learning algorithm, wherein the at leastone machine learning algorithm is trained to generate an assessmentbased on the language and based on the user activity; based on thenatural language processing, determining whether the issue meets acondition for automated resolution; if the condition is met: extracting,via an application programming interface and from the at least one userdevice, information needed to resolve the issue; and resolving, usingthe ticket management server, the issue using the extracted information;and if the condition is not met: inserting, into at least one database,a ticket comprising: an identifier associated with the at least one userdevice; and information associated with the issue to be resolved;assigning, using the ticket management server and based on theinformation associated with the issue to be resolved, a work group tothe ticket; consulting, using the ticket management server, the at leastone database to determine whether a job aid associated with the issueexists; and forwarding, to the at least one user device, at least oneof: the job aid; received communications from the work group; and anestimated amount of time to resolution based on the informationassociated with the issue to be resolved.
 2. The system of claim 1,wherein the user activity comprises user choices relative to suitabilityof the job aid.
 3. The system of claim 2, wherein assigning the workgroup to the ticket is based on the assessment.
 4. The system of claim2, wherein the operations further comprise training the machine-learningalgorithm using historical data comprising: previously receivedrequests, each request comprising user-provided issue descriptions; apreviously assigned work group for each previously received request; arecorded resolution time for each previously received request; anddocumentation associated with at least one of a product, service, orapplication.
 5. The system of claim 2, wherein the machine learningalgorithm comprises at least one of a generalized least squaresregression technique, an ordinary least squares regression technique, arandom forest regression technique, a gradient boosting regressiontechnique, or a support vector machine regression technique.
 6. Thesystem of claim 1, wherein the at least one processor is furtherconfigured to establish a communication link between the at least oneuser device and the work group, the communication link comprising adigital collaboration application.
 7. The system of claim 1, wherein thelanguage in the request comprises free form text.
 8. The system of claim1, wherein the language in the request comprises spoken language.
 9. Thesystem of claim 1, wherein analyzing the language in the requestcomprises instantiating a digital dialogue session with the at least oneuser device.
 10. The system of claim 9, wherein extracting theinformation needed to resolve the issue comprises receiving theinformation needed to resolve the issue from the at least one userdevice through the digital dialogue session.
 11. A computer-implementedmethod for efficient ticket resolution comprising: receiving, from atleast one user device, a request to resolve an issue, the requestcomprising language; capturing, using an activity logging module of aticket management server, user activity occurring at the at least oneuser device; analyzing, using the ticket management server, the languagein the request to determine the issue to be resolved by performingnatural language processing on the language wherein performing thenatural language processing comprises utilizing at least one machinelearning algorithm, wherein the at least one machine learning algorithmis trained to generate an assessment based on the language and based onthe user activity; based on the natural language processing, determiningwhether the issue meets a condition for automated resolution; if thecondition is met: extracting, via an application programming interfaceand from the at least one user device, information needed to resolve theissue; and resolving, using the ticket management server, the issueusing the extracted information; and if the condition is not met:inserting, into at least one database, a ticket comprising: anidentifier associated with the at least one user device; and informationassociated with the issue to be resolved; assigning, using the ticketmanagement server and based on the information associated with the issueto be resolved, a work group to the ticket; consulting, using the ticketmanagement server, the at least one database to determine whether a jobaid associated with the issue exists; and forwarding, to the at leastone user device, at least one of: the job aid; received communicationsfrom the work group; and an estimated amount of time to resolution basedon the information associated with the issued to be resolved.
 12. Themethod of claim 11, wherein the user activity comprises user choicesrelative to suitability of the job aid.
 13. The method of claim 12,wherein assigning the work group to the ticket is based on theassessment.
 14. The method of claim 12, further comprising training themachine-learning algorithm using historical data comprising: previouslyreceived requests, each request comprising user-provided issuedescriptions; a previously assigned work group for each previouslyreceived request; a recorded resolution time for each previouslyreceived request; and documentation associated with at least one of aproduct, service, or application.
 15. The method of claim 12, whereinthe machine learning algorithm comprises at least one of a generalizedleast squares regression technique, an ordinary least squares regressiontechnique, a random forest regression technique, a gradient boostingregression technique, or a support vector machine regression technique.16. The method of claim 11, wherein the at least one processor isfurther configured to establish a communication link between the atleast one user device and the work group, the communication linkcomprising a digital collaboration application.
 17. The method of claim11, wherein the language in the request comprises free form text. 18.The method of claim 11, wherein the language in the request comprisesspoken language.
 19. The method of claim 11, wherein analyzing thelanguage in the request comprises instantiating a digital dialoguesession with the at least one user device.
 20. The method of claim 19,wherein extracting the information needed to resolve the issue comprisesreceiving the information needed to resolve the issue from the at leastone user device through the digital dialogue session.